Offroad with Robots?

November 1, 2016

In late September, I visited Consumer Reports’ Test Track to see firsthand how Tesla’s Autopilot technology performs.

I wanted to know why Consumer Reports first granted to Tesla and then removed the highest car rating they had ever given. I also wanted to better understand the fatal crashes that have happened recently with drivers using Autopilot.

As you can see in my video above, there are indeed some serious safety and reliability problems with Autopilot. I was shocked and you’ll see that in the video.*

What’s going on here?

I have been examining AI-related products and services** over the last two years and have found almost no evidence of anyone seriously thinking about how this technology is working once in the hands of a customer. Like what happened in the early days of the web, companies are focused more on the gee whiz elements of the technology and are forgetting about what real people need or want. In the meantime, the press and analysts are either cheering machine learning or raising the specter of mass unemployment brought on by armies of robots.

Both extremes miss the point and leave out the customer.

In my keynote speech to the Councils community in NY in October, I addressed this issue head on. My talk focused on the promise and reality of artificial intelligence and the 20 year curve that most technologies go through before being designed in such a way that meet real customer needs.

The current round of AI started in the early 2000s when big data, computing power, and new algorithms were combined to turn the failed promises of AI from the 1990s into reality a short decade later. While Google has famously done a lot of work on self-driving vehicles, the company also initially made small but important changes to search. In 2004, they began testing the use of machine learning algorithms in the front-end search experience. Today, most every Google search user uses something called AutoComplete – a simple, valuable, non-intrusive AI-driven addition to search.

Last year, on the other hand, Amazon launched the Echo product, (most people refer to it as “Alexa” – that’s the name of the voice services that power Echo), a home assistant that uses a variety of technologies including voice recognition and machine learning (and, of course, Google now has a competing product).

This “conversational interface” has been widely praised in the press and potentially 3 million units have been sold (Amazon does not release official numbers). I’ve had some fun with Alexa and so have some of my early adopter friends. By the way, it’s a really great kitchen timer.

At the Council meetings, however, I walked the members through some of the challenges with Echo and with conversational interfaces in general. I got a lot of laughs as I played recordings of Alexa not understanding basic customer requests. But the message is serious. Amazon and Tesla have done great work with the technology and early adopters like me are (mostly) having fun but mainstream customers can easily get confused and frustrated (or worse).

Bottomline: don’t repeat the mistakes from the early days of the web when most companies forgot to include the customer and built websites that sucked. It’s taken 20 years – and tons of consulting work from teams like mine – but today we have websites and mobile apps that mostly work well. So, rather than going through that whole painful process again, why not shorten the 20 year curve by *starting* AI-related new product development with the customer?

* Note – Tesla has released a new version of AutoPilot, which I have not yet tested.
** In this article – following the direction of machine learning professor Pedro Domingos in his book Master Algorithm – I use different terms like ‘machine learning, ‘AI’, ‘data science’ interchangeably. And, yes, I know they are each a bit different.